U.S. patent application number 15/280454 was filed with the patent office on 2018-03-29 for autonomous vehicle: modular architecture.
The applicant listed for this patent is AutoLiv ASP, Inc., The Charles Stark Draper Laboratory, Inc.. Invention is credited to Jon Demerly, Troy Jones, Scott Lennox, John Sgueglia, Hsin-Hsiang Yang, Nicholas Alexander Zervoglos.
Application Number | 20180086336 15/280454 |
Document ID | / |
Family ID | 61687846 |
Filed Date | 2018-03-29 |
United States Patent
Application |
20180086336 |
Kind Code |
A1 |
Jones; Troy ; et
al. |
March 29, 2018 |
AUTONOMOUS VEHICLE: MODULAR ARCHITECTURE
Abstract
An architecture for an autonomous vehicle uses a top-down
approach to enable fully automated driving. The architecture is
modular and compatible with hardware from different manufacturers.
Each modular component can be tailored for individual cars, which
have different vehicle control subsystems and different sensor
subsystems.
Inventors: |
Jones; Troy; (Somerville,
MA) ; Lennox; Scott; (Arlington, MA) ;
Sgueglia; John; (Cambridge, MA) ; Demerly; Jon;
(Southfield, MI) ; Zervoglos; Nicholas Alexander;
(Acton, MA) ; Yang; Hsin-Hsiang; (Ann Arbor,
MI) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
The Charles Stark Draper Laboratory, Inc.
AutoLiv ASP, Inc. |
Cambridge
Southfield |
MA
MI |
US
US |
|
|
Family ID: |
61687846 |
Appl. No.: |
15/280454 |
Filed: |
September 29, 2016 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
B60W 30/09 20130101;
B60W 60/005 20200201; B60W 60/00272 20200201; B60W 60/00276
20200201; B60W 10/18 20130101; B60W 2554/00 20200201; G05D
2201/0213 20130101; B60W 10/20 20130101; B60W 50/02 20130101; B60W
2540/00 20130101; B60W 2710/20 20130101; B60W 60/0055 20200201;
B60W 60/0059 20200201; B60W 2710/18 20130101; B60W 2720/10
20130101; B60W 50/0097 20130101; B60W 30/0956 20130101; B60W
60/0011 20200201; B60W 10/04 20130101; B60W 2540/045 20200201; B60W
30/095 20130101; G05D 1/0088 20130101; B60W 2400/00 20130101; G05B
15/02 20130101; B60W 2900/00 20130101 |
International
Class: |
B60W 30/09 20060101
B60W030/09; G05D 1/00 20060101 G05D001/00; G05D 1/02 20060101
G05D001/02; B60W 10/04 20060101 B60W010/04; B60W 10/18 20060101
B60W010/18; B60W 10/20 20060101 B60W010/20; B60W 30/095 20060101
B60W030/095; B60W 50/02 20060101 B60W050/02; B60W 50/00 20060101
B60W050/00; G05B 15/02 20060101 G05B015/02 |
Claims
1. A method of operating an autonomous vehicle, the method
comprising: determining, at an automated driving controller (ADC),
one or more planned driving corridors that are predicted to be
drivable by the vehicle and safely separated from surrounding
vehicles and other objects; determining, at a vehicle controller
(VC), based on the one or more planned driving corridors, one or
more vehicle trajectories which are predicted to avoid collisions
with the surrounding vehicles and other objects; selecting, at the
VC, one of the determined trajectories as active based on criteria
of collision likelihood; sending steering, throttle, and braking
commands from the VC to one or more respective actuator controllers
within the vehicle to follow the active trajectory.
2. The method of claim 1, further comprising: at a perception
controller (PC), generating a stochastic prediction of free space
available for driving based on locations of observed vehicles or
other objects; at the ADC, determining the driving corridors from
the stochastic prediction of free space; at the PC, generating a
kinematic prediction of the free space available for driving by
performing kinematic-based predictions of the locations of vehicles
and objects within a threshold radius; at the VC: determining one
or more corridor trajectories in a given corridor that meet ride
comfort design goals; determining an emergency trajectory from an
updated kinematic free space prediction; selecting a nominal or
emergency trajectory based on a collision-avoidance likelihood, the
collision-avoidance likelihood being based on the updated kinematic
free space prediction; generating updated steering, throttle, and
braking commands to follow the selected trajectory corridor; and
sending updated commands to the actuator controllers for
execution.
3. The method of claim 1, further comprising: at the ADC: planning
a route of roadways for the autonomous vehicle to travel to a
destination, wherein determining the corridor within the route of
roadways is based on information determined by a plurality of
sensors about a driving surface and objects surrounding the
autonomous vehicle.
4. The method of claim 1, further comprising: translating inputs
from a plurality of sensor subsystems to a vendor-neutral format at
a Sensor Interface Server (SIS).
5. The method of claim 1, further comprising: determining a
position and an attitude of the autonomous vehicle from a plurality
of sensor inputs at a Localization Controller (LC).
6. The method of claim 1, further comprising: determining objects
around the autonomous vehicle and on drivable surfaces, detected by
a plurality of sensor subsystems, at a Perception Controller
(PC).
7. The method of claim 1, further comprising: providing an
interface for interaction between an operator, passengers, and
humans external to the vehicle at a Human Interaction Controller
(HIC).
8. The method of claim 1, further comprising: interacting with
other-self driving cars or automated systems through a Machine
Interaction Controller (MC).
9. The method of claim 1, further comprising: coordinating
exchanges, at a System Controller (SC), of control between an
operator and elements of the autonomous vehicle.
10. The method of claim 9, further comprising, monitoring the
elements of the autonomous vehicle for failures or other abnormal
behavior, managing corrective actions to resolve failures.
11. The method of claim 1, further comprising: at the VC, driving
outside of the ADC determined corridor in response to determining a
likelihood of collision within that corridor.
12. The method of claim 1, further comprising: minimizing use of
communication bandwidth in an architecture of the autonomous
vehicle by providing: a sensor interface server (SIS) configured to
translate and filter sensor data sent to other elements of the
architecture; a perception controller (PC) configured to fuse
sensor measurements from a plurality of sensors into a single
estimate of perceptions of the environment around the autonomous
vehicle; and a localization controller (LC) configured to fuse
outputs from multiple sensor measurements into a single position
and attitude of the autonomous vehicle in the world.
13. The method of claim 1, further comprising measuring
availability of the operator of the autonomous vehicle to assist in
driving the vehicle; and providing a variable level of automated
function by the autonomous vehicle based on the measured
availability of the operator at a human interface controller
(HC).
14. The method of claim 1, further comprising: at a human interface
controller (HC): translating internal status of the autonomous
vehicle to a human understandable format; and presenting the
translated internal status in the human understandable format to
the operator.
15. A system for operating an autonomous vehicle, the system
comprising: an Automated Driving Controller (ADC) configured to
determine, one or more planned driving corridors that are predicted
to be drivable by the vehicle and safely separated from surrounding
vehicles and other objects; and a vehicle controller (VC)
configured to: determine, based on the one or more planned driving
corridors, one or more vehicle trajectories which are predicted to
avoid collisions with the surrounding vehicles and other objects;
select one of the determined trajectories as active based on
criteria of collision likelihood; and send steering, throttle, and
braking commands to one or more respective actuator controllers
within the vehicle to follow the active trajectory
16. The system of claim 15, wherein: a perception controller (PC)
configured to generate a stochastic prediction of free space
available for driving based on locations of observed vehicles or
other objects; wherein the ADC is further configured to determine
the driving corridors from the stochastic prediction of free space;
wherein the PC is further configured to generate a kinematic
prediction of the free space available for driving by performing
kinematic-based predictions of the locations of vehicles and
objects within a threshold radius; wherein the VC is further
configured to: determine one or more corridor trajectories in a
given corridor that meet ride comfort design goals; determine an
emergency trajectory corridor from an updated kinematic free space
prediction; select a nominal or emergency trajectory based on a
collision-avoidance likelihood, the collision-avoidance likelihood
being based on the updated kinematic free space prediction;
generate updated steering, throttle, and braking commands to follow
the selected trajectory corridor; and send updated commands to the
actuator controllers for execution.
17. The system of claim 15, wherein the ADC is further configured
to plan a route of roadways for the autonomous vehicle to travel to
a destination, wherein determining the corridor within the route of
roadways is based on information determined by a plurality of
sensors about the driving surface and objects surrounding the
autonomous vehicle.
18. The system of claim 15, further comprising: a sensor interface
server (SIS) configured to translate inputs from a plurality of
sensor subsystems to a vendor-neutral format.
19. The system of claim 15, further comprising: a localization
controller configured to determine a position and an attitude of
the autonomous vehicle from a plurality of sensor inputs.
20. The system of claim 15, further comprising: a perception
controller (PC) configured to determine objects around the
autonomous vehicle and on drivable surfaces, detected by a
plurality of sensor subsystems.
21. The system of claim 15, further comprising: a human interaction
controller (HC) configured to provide an interface for interaction
between an operator, passengers, and humans external to the
vehicle.
22. The system of claim 16, further comprising: machine interaction
controller (MC) configured to interact with other-self driving cars
or automated systems.
23. The system of claim 15, further comprising: a system controller
(SC) configured to coordinate exchanges of control between the
operator and elements of the modular architecture of the autonomous
vehicle.
24. The system of claim 23, wherein the SC is further configured to
monitor the elements of the autonomous vehicle for failures or
other abnormal behavior, managing corrective actions to resolve
failures.
25. The system of claim 15, wherein the vehicle controller is
further configured to drive outside the ADC generated corridor in
response to determining a high likelihood of collision within that
corridor.
26. The system of claim 15, further comprising: a sensor interface
server (SIS) configured to translate and filter sensor data sent to
other elements of the architecture; a perception controller (PC)
configured to fuse sensor measurements from the plurality of
sensors into a single estimate of perceptions of the environment
around the autonomous vehicle; and a localization controller (LC)
configured to fuse outputs from multiple sensors measurements into
a single position and attitude of the autonomous vehicle in the
world; wherein the sensor interface server, perception controller,
and localization controller minimize use of communication bandwidth
in an architecture of the autonomous vehicle.
27. The system of claim 15, further comprising a human interaction
controller (HC) configured to measure availability of the operator
of the autonomous vehicle to assist in driving the vehicle, and
provide a variable level of automated function by the autonomous
vehicle based on the measured availability of the operator.
28. The system of claim 15, further comprising: a human interaction
controller (HC) configured to translate internal status of the
autonomous vehicle to a human-understandable format, and present
the translated internal status in the human-understandable format
to the operator.
Description
BACKGROUND
[0001] Currently, vehicles can employ automated systems such as
lane assist, pre-collision breaking, and rear cross-track
detection. These systems can assist a driver of the vehicle from
making human error and to avoid crashes with other vehicles, moving
objects, or pedestrians. However, these systems only automate
certain vehicle functions, and still rely on the driver of the
vehicle for other operations.
SUMMARY
[0002] In an embodiment, a method of operating an autonomous
vehicle includes determining, at an automated driving controller
(ADC), one or more planned driving corridors that are predicted to
be drivable by the vehicle and safely separated from surrounding
vehicles and other objects. The method further includes
determining, at a vehicle controller (VC), based on the one or more
planned driving corridors, one or more vehicle trajectories which
are predicted to avoid collisions with the surrounding vehicles and
other objects. The method further includes selecting, at the VC,
one of the determined trajectories as active based on criteria of
collision likelihood. The method further includes sending steering,
throttle, and braking commands from the VC to one or more
respective actuator controllers within the vehicle to follow the
active trajectory.
[0003] In another embodiment the method further includes, at a
perception controller (PC), generating a stochastic prediction of
free space available for driving based on locations of observed
vehicles or other objects. The method further includes, at the ADC,
determining the driving corridors from the stochastic prediction of
free space. The method further includes, at the PC, generating a
kinematic prediction of the free space available for driving by
performing kinematic-based predictions of the locations of vehicles
and objects within a threshold radius. The method further includes,
at the VC, determining one or more corridor trajectories in a given
corridor that meet ride comfort design goals, determining an
emergency trajectory from an updated kinematic free space
prediction, selecting a nominal or emergency trajectory based on a
collision-avoidance likelihood, the collision-avoidance likelihood
being based on the updated kinematic free space prediction,
generating updated steering, throttle, and braking commands to
follow the selected trajectory corridor, and sending updated
commands to the actuator controllers for execution.
[0004] In a further embodiment, the method includes, at the ADC,
planning a route of roadways for the autonomous vehicle to travel
to a destination, wherein determining the corridor within the route
of roadways is based on information determined by a plurality of
sensors about a driving surface and objects surrounding the
autonomous vehicle.
[0005] In a further embodiment, the method further includes
translating inputs from a plurality of sensor subsystems to a
vendor-neutral format at a Sensor Interface Server (SIS).
[0006] In a further embodiment, the method includes determining a
position and an attitude of the autonomous vehicle from a plurality
of sensor inputs at a Localization Controller (LC).
[0007] In a further embodiment, the method includes determining
objects around the autonomous vehicle and on drivable surfaces,
detected by a plurality of sensor subsystems, at a Perception
Controller (PC).
[0008] In a further embodiment, the method includes providing an
interface for interaction between an operator, passengers, and
humans external to the vehicle at a Human Interaction Controller
(HIC).
[0009] In a further embodiment, the method includes interacting
with other-self driving cars or automated systems through a Machine
Interaction Controller (MC).
[0010] In a further embodiment, the method includes coordinating
exchanges, at a System Controller (SC), of control between an
operator and elements of the autonomous vehicle. The system
controller can further monitor the elements of the autonomous
vehicle for failures or other abnormal behavior, managing
corrective actions to resolve failures.
[0011] In a further embodiment, the method can include, at the VC,
driving outside of the ADC determined corridor in response to
determining a likelihood of collision within that corridor.
[0012] In a further embodiment, the method includes minimizing use
of communication bandwidth in an architecture of the autonomous
vehicle by providing a sensor interface server (SIS) configured to
translate and filter sensor data sent to other elements of the
architecture, a perception controller (PC) configured to fuse
sensor measurements from a plurality of sensors into a single
estimate of perceptions of the environment around the autonomous
vehicle, and a localization controller (LC) configured to fuse
outputs from multiple sensor measurements into a single position
and attitude of the autonomous vehicle in the world.
[0013] In a further embodiment, the method includes measuring
availability of the operator of the autonomous vehicle to assist in
driving the vehicle and providing a variable level of automated
function by the autonomous vehicle based on the measured
availability of the operator at a human interface controller
(HC).
[0014] In a further embodiment, the method includes, at a human
interface controller (HC), translating internal status of the
autonomous vehicle to a human-understandable format (e.g., visual,
audio, haptic), and presenting the translated internal status in
the human-understandable format to the operator.
[0015] In an embodiment, a system for operating an autonomous
vehicle includes an Automated Driving Controller (ADC) configured
to determine, one or more planned driving corridors that are
predicted to be drivable by the vehicle and safely separated from
surrounding vehicles and other objects. The system further includes
a vehicle controller (VC) configured to determine, based on the one
or more planned driving corridors, one or more vehicle trajectories
which are predicted to avoid collisions with the surrounding
vehicles and other objects, select one of the determined
trajectories as active based on criteria of collision likelihood,
and send steering, throttle, and braking commands to one or more
respective actuator controllers within the vehicle to follow the
active trajectory.
[0016] In an embodiment, the system includes a perception
controller (PC) configured to generate a stochastic prediction of
free space available for driving based on locations of observed
vehicles or other objects. The ADC is further configured to
determine the driving corridors from the stochastic prediction of
free space. The PC is further configured to generate a kinematic
prediction of the free space available for driving by performing
kinematic-based predictions of the locations of vehicles and
objects within a threshold radius. The VC is further configured to
determine one or more corridor trajectories in a given corridor
that meet ride comfort design goals, determine an emergency
trajectory corridor from an updated kinematic free space
prediction, select a nominal or emergency trajectory based on a
collision-avoidance likelihood, the collision-avoidance likelihood
being based on the updated kinematic free space prediction,
generate updated steering, throttle, and braking commands to follow
the selected trajectory corridor, and send updated commands to the
actuator controllers for execution.
BRIEF DESCRIPTION OF THE DRAWINGS
[0017] The foregoing will be apparent from the following more
particular description of example embodiments of the invention, as
illustrated in the accompanying drawings in which like reference
characters refer to the same parts throughout the different views.
The drawings are not necessarily to scale, emphasis instead being
placed upon illustrating embodiments of the present invention.
[0018] FIG. 1 is a diagram illustrating steps in an embodiment of
an automated control system of the Observe, Orient, Decide, and Act
(OODA) model.
[0019] FIG. 2 is a block diagram of an embodiment of an autonomous
vehicle high-level architecture.
[0020] FIG. 3 is a block diagram illustrating an embodiment of the
sensor interaction controller (SIC), perception controller (PC),
and localization controller (LC).
[0021] FIG. 4 is a block diagram illustrating an example embodiment
of the automatic driving controller (ADC), vehicle controller (VC)
and actuator controller.
[0022] FIG. 5 is a diagram illustrating decision time scales of the
ADC and VC.
[0023] FIG. 6 is a block diagram illustrating an example embodiment
of the system controller, human interface controller (HC) and
machine interface controller (MC).
[0024] FIG. 7 illustrates a computer network or similar digital
processing environment in which embodiments of the present
invention may be implemented.
[0025] FIG. 8 is a diagram of an example internal structure of a
computer (e.g., client processor/device or server computers in the
computer system of FIG. 7.
DETAILED DESCRIPTION
[0026] A description of example embodiments of the invention
follows.
[0027] FIG. 1 is a diagram illustrating steps in an embodiment of
an automated control system of the Observe, Orient, Decide, and Act
(OODA) model. Automated systems, such as highly-automated driving
systems, or, self-driving cars, or autonomous vehicles, employ an
OODA model. The observe virtual layer 102 involves sensing features
from the world using machine sensors, such as laser ranging, radar,
infra-red, vision systems, or other systems. The orientation
virtual layer 104 involves perceiving situational awareness based
on the sensed information. Examples of orientation virtual layer
activities are Kalman filtering, model based matching, machine or
deep learning, and Bayesian predictions. The decide virtual layer
106 selects an action from multiple objects to a final decision.
The act virtual layer 108 provides guidance and control for
executing the decision. FIG. 2 is a block diagram 200 of an
embodiment of an autonomous vehicle high-level architecture 206.
The architecture 206 is built using a top-down approach to enable
fully automated driving. Further, the architecture 206 is
preferably modular such that it can be adaptable with hardware from
different vehicle manufacturers. The architecture 206, therefore,
has several modular elements functionally divided to maximize these
properties. In an embodiment, the modular architecture 206
described herein can interface with sensor systems 202 of any
vehicle 204. Further, the modular architecture 206 can receive
vehicle information from and communicate with any vehicle 204.
[0028] Elements of the modular architecture 206 include sensors
202, Sensor Interface Controller (SIC) 208, localization controller
(LC) 210, perception controller (PC) 212, automated driving
controller 214 (ADC), vehicle controller 216 (VC), system
controller 218 (SC), human interaction controller 220 (HC) and
machine interaction controller 222 (MC).
[0029] Referring again to the OODA model of FIG. 1, in terms of an
autonomous vehicle, the observation layer of the model includes
gathering sensor readings, for example, from vision sensors, Radar
(Radio Detection And Ranging), LIDAR (Light Detection And Ranging),
and Global Positioning Systems (GPS). The sensors 202 shown in FIG.
2 shows such an observation layer. Examples of the orientation
layer of the model can include determining where a car is relative
to the world, relative to the road it is driving on, and relative
to lane markings on the road, shown by Perception Controller (PC)
212 and Localization Controller (LC) 210 of FIG. 2. Examples of the
decision layer of the model include determining a corridor to
automatically drive the car, and include elements such as the
Automatic Driving Controller (ADC) 214 and Vehicle Controller (VC)
216 of FIG. 2. Examples of the act layer include converting that
corridor into commands to the vehicle's driving systems (e.g.,
steering sub-system, acceleration sub-system, and breaking
sub-system) that direct the car along the corridor, such as
actuator control 410 of FIG. 4. A person of ordinary skill in the
art can recognize that the layers of the system are not strictly
sequential, and as observations change, so do the results of the
other layers. For example, after the system chooses a corridor to
drive in, changing conditions on the road, such as detection of
another object, may direct the car to modify its corridor, or enact
emergency procedures to prevent a collision. Further, the commands
of the vehicle controller may need to be adjusted dynamically to
compensate for drift, skidding, or other changes to expected
vehicle behavior.
[0030] At a high level, the module architecture 206 receives
measurements from sensors 202. While different sensors may output
different sets of information in different formats, the modular
architecture 206 includes Sensor Interface Controller (SIC) 208,
sometimes also referred to as a Sensor Interface Server (SIS),
configured to translate the sensor data into data having a
vendor-neutral format that can be read by the modular architecture
206. Therefore, the modular architecture 206 learns about the
environment around the vehicle 204 from the vehicle's sensors, no
matter the vendor, manufacturer, or configuration of the sensors.
The SIS 208 can further tag each sensor's data with a metadata tag
having its location and orientation in the car, which can be used
by the perception controller to determine the unique angle,
perspective, and blind spot of each sensor.
[0031] Further, the modular architecture 206 includes vehicle
controller 216 (VC). The VC 216 is configured to send commands to
the vehicle and receive status messages from the vehicle. The
vehicle controller 216 receives status messages from the vehicle
204 indicating the vehicle's status, such as information regarding
the vehicle's speed, attitude, steering position, braking status,
and fuel level, or any other information about the vehicle's
subsystems that is relevant for autonomous driving. The modular
architecture 206, based on the information from the vehicle 204 and
the sensors 202, therefore can calculate commands to send from the
VC 216 to the vehicle 204 to implement self-driving. The functions
of the various modules within the modular architecture 206 are
described in further detail below. However, when viewing the
modular architecture 206 at a high level, it receives (a) sensor
information from the sensors 202 and (b) vehicle status information
from the vehicle 204, and in turn, provides the vehicle
instructions to the vehicle 204. Such an architecture allows the
modular architecture to be employed for any vehicle with any sensor
configuration. Therefore, any vehicle platform that includes a
sensor subsystem (e.g., sensors 202) and an actuation subsystem
having the ability to provide vehicle status and accept driving
commands (e.g., actuator control 410 of FIG. 4) can integrate with
the modular architecture 206.
[0032] Within the modular architecture 206, various modules work
together to implement automated driving according to the OODA
model. The sensors 202 and SIC 208 reside in the "observe" virtual
layer. As described above, the SIC 208 receives measurements (e.g.,
sensor data) having various formats. The SIC 208 is configured to
convert vendor-specific data directly from the sensors to
vendor-neutral data. In this way, the set of sensors 202 can
include any brand of Radar, LIDAR, image sensor, or other sensors,
and the modular architecture 206 can use their perceptions of the
environment effectively.
[0033] The measurements output by the sensor interface server are
then processed by perception controller (PC) 212 and localization
controller (LC) 210. The PC 212 and LC 210 both reside in the
"orient" virtual layer of the OODA model. The LC 210 determines a
robust world-location of the vehicle that can be more precise than
a GPS signal, and still determines the world-location of the
vehicle when there is no available or an inaccurate GPS signal. The
LC 210 determines the location based on GPS data and sensor data.
The PC 212, on the other hand, generates prediction models
representing a state of the environment around the car, including
objects around the car and state of the road. FIG. 3 provides
further details regarding the SIC 208, LC 210 and PC 212.
[0034] Automated driving controller 214 (ADC) and vehicle
controller 216 (VC) receive the outputs of the perception
controller and localization controller. The ADC 214 and VC 216
reside in the "decide" virtual layer of the OODA model. The ADC 214
is responsible for destination selection, route and lane guidance,
and high-level traffic surveillance. The ADC 214 further is
responsible for lane selection within the route, and identification
of safe harbor areas to diver the vehicle in case of an emergency.
In other words, the ADC 214 selects a route to reach the
destination, and a corridor within the route to direct the vehicle.
The ADC 214 passes this corridor onto the VC 216. Given the
corridor, the VC 216 provides a trajectory and lower level driving
functions to direct the vehicle through the corridor safely. The VC
216 first determines the best trajectory to maneuver through the
corridor while providing comfort to the driver, an ability to reach
safe harbor, emergency maneuverability, and ability to follow the
vehicle's current trajectory. In emergency situations, the VC 216
overrides the corridor provided by the ADC 214 and immediately
guides the car into a safe harbor corridor, returning to the
corridor provided by the ADC 214 when it is safe to do so. The VC
216, after determining how to maneuver the vehicle, including
safety maneuvers, then provides actuation commands to the vehicle
204, which executes the commands in its steering, throttle, and
braking subsystems. This element of the VC 216 is therefore in the
"act" virtual layer of the CODA model. FIG. 4 describes the ADC 214
and VC 216 in further detail.
[0035] The modular architecture 206 further coordinates
communication with various modules through system controller 218
(SC). By exchanging messages with the ADC 214 and VC 216, the SC
218 enables operation of human interaction controller 220 (HC) and
machine interaction controller 222 (MC). The HC 220 provides
information about the autonomous vehicle's operation in a human
understandable format based on status messages coordinated by the
system controller. The HC 220 further allows for human input to be
factored into the car's decisions. For example, the HC 220 enables
the operator of the vehicle to enter or modify the destination or
route of the vehicle, as one example. The SC 218 interprets the
operator's input and relays the information to the VC 216 or ADC
214 as necessary.
[0036] Further, the MC 222 can coordinate messages with other
machines or vehicles. For example, other vehicles can
electronically and wirelessly transmit route intentions, intended
corridors of travel, and sensed objects that may be in other
vehicle's blind spot to autonomous vehicles, and the MC 222 can
receive such information, and relay it to the VC 216 and ADC 214
via the SC 218. In addition, the MC 222 can send information to
other vehicles wirelessly. In the example of a turn signal, the MC
222 can receive a notification that the vehicle intends to turn.
The MC 222 receives this information via the VC 216 sending a
status message to the SC 218, which relays the status to the MC
222. However, other examples of machine communication can also be
implemented. For example, other vehicle sensor information or
stationary sensors can wirelessly send data to the autonomous
vehicle, giving the vehicle a more robust view of the environment.
Other machines may be able to transmit information about objects in
the vehicles blind spot, for example. In further examples, other
vehicles can send their vehicle track. In an even further examples,
traffic lights can send a digital signal of their status to aid in
the case where the traffic light is not visible to the vehicle. A
person of ordinary skill in the art can recognize that any
information employed by the autonomous vehicle can also be
transmitted to or received from other vehicles to aid in autonomous
driving. FIG. 6 shows the HC 220, MC 222, and SC 218 in further
detail.
[0037] FIG. 3 is a block diagram 300 illustrating an embodiment of
the sensor interaction controller 304 (SIC), perception controller
(PC) 306, and localization controller (LC) 308. A sensor array 302
of the vehicle can include various types of sensors, such as a
camera 302a, radar 302b, LIDAR 302c, GPS 302d, IMU 302e, or
vehicle-to-everything (V2X) 302f. Each sensor sends individual
vendor defined data types to the SIC 304. For example, the camera
302a sends object lists and images, the radar 302b sends object
lists, and in-phase/quadrature (IQ) data, the LIDAR 302c sends
object lists and scan points, the GPS 302d sends position and
velocity, the IMU 302e sends acceleration data, and the V2X 302f
controller sends tracks of other vehicles, turn signals, other
sensor data, or traffic light data. A person of ordinary skill in
the art can recognize that the sensor array 302 can employ other
types of sensors, however. The SIC 304 monitors and diagnoses
faults at each of the sensors 302a-f. In addition, the SIC 304
isolates the data from each sensor from its vendor specific package
and sends vendor neutral data types to the perception controller
(PC) 306 and localization controller 308 (LC). The SIC 304 forwards
localization feature measurements and position and attitude
measurements to the LC 308, and forwards tracked object
measurements, driving surface measurements, and position &
attitude measurements to the PC 306. The SIC 304 can further be
updated with firmware so that new sensors having different formats
can be used with the same modular architecture.
[0038] The LC 308 fuses GPS and IMU data with Radar, Lidar, and
Vision data to determine a vehicle location, velocity, and attitude
with more precision than GPS can provide alone. The LC 308 then
reports that robustly determined location, velocity, and attitude
to the PC 306. The LC 308 further monitors measurements
representing position, velocity, and attitude data for accuracy
relative to each other, such that if one sensor measurement fails
or becomes degraded, such as a GPS signal in a city, the LC 308 can
correct for it. The PC 306 identifies and locates objects around
the vehicle based on the sensed information. The PC 306 further
estimates drivable surface regions surrounding the vehicle, and
further estimates other surfaces such as road shoulders or drivable
terrain in the case of an emergency. The PC 306 further provides a
stochastic prediction of future locations of objects. The PC 306
further stores a history of objects and drivable surfaces.
[0039] The PC 306 outputs two predictions, a strategic prediction,
and a tactical prediction. The tactical prediction represents the
world around 2-4 seconds into the future, which only predicts the
nearest traffic and road to the vehicle. This prediction includes a
free space harbor on shoulder of the road or other location. This
tactical prediction is based entirely on measurements from sensors
on the vehicle of nearest traffic and road conditions.
[0040] The strategic prediction is a long term prediction that
predicts areas of the car's visible environment beyond the visible
range of the sensors. This prediction is for greater than four
seconds into the future, but has a higher uncertainty than the
tactical prediction because objects (e.g., cars and people) may
change their currently observed behavior in an unanticipated
manner. Such a prediction can also be based on sensor measurements
from external sources including other autonomous vehicles, manual
vehicles with a sensor system and sensor communication network,
sensors positioned near or on the roadway or received over a
network from transponders on the objects, and traffic lights,
signs, or other signals configured to communicate wirelessly with
the autonomous vehicle.
[0041] FIG. 4 is a block diagram 400 illustrating an example
embodiment of the automatic driving controller (ADC) 402, vehicle
controller (VC) 404 and actuator controller 410. The ADC 402 and VC
404 execute the "decide" virtual layer of the CODA model.
[0042] The ADC 402, based on destination input by the operator and
current position, first creates an overall route from the current
position to the destination including a list of roads and junctions
between roads in order to reach the destination. This strategic
route plan may be based on traffic conditions, and can change based
on updating traffic conditions, however such changes are generally
enforced for large changes in estimated time of arrival (ETA).
Next, the ADC 402 plans a safe, collision-free, corridor for the
autonomous vehicle to drive through based on the surrounding
objects and permissible drivable surface--both supplied by the PC.
This corridor is continuously sent as a request to the VC 404 and
is updated as traffic and other conditions change. The VC 404
receives the updates to the corridor in real time. The ADC 402
receives back from the VC 404 the current actual trajectory of the
vehicle, which is also used to modify the next planned update to
the driving corridor request.
[0043] The ADC 402 generates a strategic corridor for the vehicle
to navigate. The ADC 402 generates the corridor based on
predictions of the free space on the road in the strategic/tactical
prediction. The ADC 402 further receives the vehicle position
information and vehicle attitude information from the perception
controller of FIG. 3. The VC 404 further provides the ADC 402 with
an actual trajectory of the vehicle from the vehicle's actuator
control 410. Based on this information, the ADC 402 calculates
feasible corridors to drive the road, or any drivable surface. In
the example of being on an empty road, the corridor may follow the
lane ahead of the car.
[0044] In another example of the car needing to pass out a car, the
ADC 402 can determine whether there is free space in a passing lane
and in front of the car to safely execute the pass. The ADC 402 can
automatically calculate based on (a) the current distance to the
car to be passed, (b) amount of drivable road space available in
the passing lane, (c) amount of free space in front of the car to
be passed, (d) speed of the vehicle to be passed, (e) current speed
of the autonomous vehicle, and (f) known acceleration of the
autonomous vehicle, a corridor for the vehicle to travel through to
execute the pass maneuver.
[0045] In another example, the ADC 402 can determine a corridor to
switch lanes when approaching a highway exit. In addition to all of
the above factors, the ADC 402 monitors the planned route to the
destination and, upon approaching a junction, calculates the best
corridor to safely and legally continue on the planned route.
[0046] The ADC 402 the provides the requested corridor 406 to the
VC 404, which works in tandem with the ADC 402 to allow the vehicle
to navigate the corridor. The requested corridor 406 places
geometric and velocity constraints on any planned trajectories for
a number of seconds into the future. The VC 404 determines a
trajectory to maneuver within the corridor 406. The VC 404 bases
its maneuvering decisions from the tactical/maneuvering prediction
received from the perception controller and the position of the
vehicle and the attitude of the vehicle. As described previously,
the tactical/maneuvering prediction is for a shorter time period,
but has less uncertainty. Therefore, for lower-level maneuvering
and safety calculations, the VC 404 effectively uses the
tactical/maneuvering prediction to plan collision-free trajectories
within requested corridor 406. As needed in emergency situations,
the VC 404 plans trajectories outside the corridor 406 to avoid
collisions with other objects.
[0047] The VC 404 then determines, based on the requested corridor
406, the current velocity and acceleration of the car, and the
nearest objects, how to drive the car through that corridor 406
while avoiding collisions with objects and remain on the drivable
surface. The VC 404 calculates a tactical trajectory within the
corridor, which allows the vehicle to maintain a safe separation
between objects. The tactical trajectory also includes a backup
safe harbor trajectory in the case of an emergency, such as a
vehicle unexpectedly decelerating or stopping, or another vehicle
swerving in front of the autonomous vehicle.
[0048] As necessary to avoid collisions, the VC 404 may be required
to command a maneuver suddenly outside of the requested corridor
from the ADC 402. This emergency maneuver can be initiated entirely
by the VC 404 as it has faster response times than the ADC 402 to
imminent collision threats. This capability isolates the safety
critical collision avoidance responsibility within the VC 404. The
VC 404 sends maneuvering commands to the actuators that control
steering, throttling, and braking of the vehicle platform.
[0049] The VC 404 executes its maneuvering strategy by sending a
current vehicle trajectory 408 having driving commands (e.g.,
steering, throttle, braking) to the vehicle's actuator controls
410. The vehicle's actuator controls 410 apply the commands to the
car's respective steering, throttle, and braking systems. The VC
404 sending the trajectory 408 to the actuator controls represent
the "Act" virtual layer of the CODA model. By conceptualizing the
autonomous vehicle architecture in this way, the VC is the only
component needing configuration to control a specific model of car
(e.g., format of each command, acceleration performance, turning
performance, and braking performance), whereas the ADC remaining
highly agnostic to the specific vehicle capacities. In an example,
the VC 404 can be updated with firmware configured to allow
interfacing with particular vehicle's actuator control systems, or
a fleet-wide firmware update for all vehicles.
[0050] FIG. 5 is a diagram 500 illustrating decision time scales of
the ADC 402 and VC 404. The ADC 402 implements higher-level,
strategic 502 and tactical 504 decisions by generating the
corridor. The ADC 402 therefore implements the decisions having a
longer range/or time scale. The estimate of world state used by the
ADC 402 for planning strategic routes and tactical driving
corridors for behaviors such as passing or making turns has higher
uncertainty, but predicts longer into the future, which is
necessary for planning these autonomous actions. The strategic
predictions have high uncertainty because they predict beyond the
sensor's visible range, relying solely on non-vision technologies,
such as Radar, for predictions of objects far away from the car,
that events can change quickly due to, for example, a human
suddenly changing his or her behavior, or the lack of visibility of
objects beyond the visible range of the sensors. Many tactical
decisions, such as passing a car at highway speed, require
perception Beyond the Visible Range (BVR) of an autonomous vehicle
(e.g., 100 m or greater), whereas all maneuverability 506 decisions
are made based on locally perceived objects to avoid
collisions.
[0051] The VC 404, on the other hand, generates maneuverability
decisions 506 using maneuverability predictions that are short time
frame/range predictions of object behaviors and the driving
surface. These maneuverability predictions have a lower uncertainty
because of the shorter time scale of the predictions, however, they
rely solely on measurements taken within visible range of the
sensors on the autonomous vehicle. Therefore, the VC 404 uses these
maneuverability predictions (or estimates) of the state of the
environment immediately around the car for fast response planning
of collision-free trajectories for the autonomous vehicle. The VC
402 issues actuation commands, on the lowest end of the time scale,
representing the execution of the already planned corridor and
maneuvering through the corridor.
[0052] FIG. 6 is a block diagram 600 illustrating an example
embodiment of the system controller 602, human interface controller
604 (HC) and machine interface controller 606 (MC). The human
interaction controller 604 (HC) receives input command requests
from the operator. The HC 604 also provides outputs to the
operator, passengers of the vehicle, and humans external to the
autonomous vehicle. The HC 604 provides the operator and passengers
(via visual, audio, haptic, or other interfaces) a
human-understandable representation of the system status and
rationale of the decision making of the autonomous vehicle. For
example, the HC 604 can display the vehicle's long-term route, or
planned corridor and safe harbor areas. Additionally, the HC 604
reads sensor measurements about the state of the driver, allowing
the HC 604 to monitor the availability of the driver to assist with
operations of the car at any time. As one example, a sensor system
within the vehicle could sense whether the operator has hands on
the steering wheel. If so, the HC 604 can signal that a transition
to operator steering can be allowed, but otherwise, the HC 604 can
prevent a turnover of steering controls to the operator. In another
example, the HC 604 can synthesize and summarize decision making
rationale to the operator, such as reasons why it selected a
particular route. As another example, a sensor system within the
vehicle can monitor the direction the driver is looking. The HC 604
can signal that a transition to driver operation is allowed if the
driver is looking at the road, but if the driver is looking
elsewhere, the system does not allow operator control. In a further
embodiment, the HC 604 can take over control, or emergency only
control, of the vehicle while the operator checks the vehicle's
blind spot and looks away from the windshield.
[0053] The machine interaction controller 606 (MC) interacts with
other autonomous vehicles or automated system to coordinate
activities such as formation driving or traffic management. The MC
606 reads the internal system status and generates an output data
type that can be read by collaborating machine systems, such as the
V2X data type. This status can be broadcast over a network by
collaborating systems. The MC 606 can translate any command
requests from external machine systems (e.g., slow down, change
route, merge request, traffic signal status) into commands requests
routed to the SC for arbitration against the other command requests
from the HC 604. The MC 606 can further authenticate (e.g., using
signed messages from other trusted manufacturers) messages from
other systems to ensure that they are valid and represent the
environment around the car. Such an authentication can prevent
tampering from hostile actors.
[0054] The system controller 602 (SC) serves as an overall manager
of the elements within the architecture. The SC 602 aggregates the
status data from all of the system elements to determine total
operational status, and sends commands to the elements to execute
system functions. If elements of the system report failures, the SC
602 initiates diagnostic and recovery behaviors to ensure
autonomous operation such that the vehicle remains safe. Any
transitions of the vehicle to/from an automated state of driving
are approved or denied by the SC 602 pending the internal
evaluation of operational readiness for automated driving and the
availability of the human driver.
[0055] FIG. 7 illustrates a computer network or similar digital
processing environment in which embodiments of the present
invention may be implemented.
[0056] Client computer(s)/devices 50 and server computer(s) 60
provide processing, storage, and input/output devices executing
application programs and the like. The client computer(s)/devices
50 can also be linked through communications network 70 to other
computing devices, including other client devices/processes 50 and
server computer(s) 60. The communications network 70 can be part of
a remote access network, a global network (e.g., the Internet), a
worldwide collection of computers, local area or wide area
networks, and gateways that currently use respective protocols
(TCP/IP, Bluetooth.RTM., etc.) to communicate with one another.
Other electronic device/computer network architectures are
suitable.
[0057] FIG. 8 is a diagram of an example internal structure of a
computer (e.g., client processor/device 50 or server computers 60)
in the computer system of FIG. 7. Each computer 50, 60 contains a
system bus 79, where a bus is a set of hardware lines used for data
transfer among the components of a computer or processing system.
The system bus 79 is essentially a shared conduit that connects
different elements of a computer system (e.g., processor, disk
storage, memory, input/output ports, network ports, etc.) that
enables the transfer of information between the elements. Attached
to the system bus 79 is an I/O device interface 82 for connecting
various input and output devices (e.g., keyboard, mouse, displays,
printers, speakers, etc.) to the computer 50, 60. A network
interface 86 allows the computer to connect to various other
devices attached to a network (e.g., network 70 of FIG. 7). Memory
90 provides volatile storage for computer software instructions 92
and data 94 used to implement an embodiment of the present
invention (e.g., sensor interface controller, perception
controller, localization controller, automated driving controller,
vehicle controller, system controller, human interaction
controller, and machine interaction controller detailed above).
Disk storage 95 provides non-volatile storage for computer software
instructions 92 and data 94 used to implement an embodiment of the
present invention. A central processor unit 84 is also attached to
the system bus 79 and provides for the execution of computer
instructions.
[0058] In one embodiment, the processor routines 92 and data 94 are
a computer program product (generally referenced 92), including a
non-transitory computer-readable medium (e.g., a removable storage
medium such as one or more DVD-ROM's, CD-ROM's, diskettes, tapes,
etc.) that provides at least a portion of the software instructions
for the invention system. The computer program product 92 can be
installed by any suitable software installation procedure, as is
well known in the art. In another embodiment, at least a portion of
the software instructions may also be downloaded over a cable
communication and/or wireless connection. In other embodiments, the
invention programs are a computer program propagated signal product
embodied on a propagated signal on a propagation medium (e.g., a
radio wave, an infrared wave, a laser wave, a sound wave, or an
electrical wave propagated over a global network such as the
Internet, or other network(s)). Such carrier medium or signals may
be employed to provide at least a portion of the software
instructions for the present invention routines/program 92.
* * * * *